Bibliographic record
Abstract
In Reply We thank Fernandes et al for their interest in our study 1 and agree that this field requires further exploration.Explanatory trials are primed to maximize the likelihood of finding efficacy of an intervention by testing it in an ideal setting, whereas pragmatic trials aim to test effectiveness of an intervention in a more generalizable setting.Hence, they are expected to generate more generalizable results, with the risk understood that there may be more variation in less tightly controlled environments, which may result in differing results.In our study, 1 380 of 616 randomized clinical trials (61.7%) were positive for the primary end point, 56 (9.1%) were neutral for the primary end point but positive for at least one secondary end point, and 180 (29.2%) were neutral for both the primary and secondary end points.The proportion of trials with positive results was fairly stable over time, with 113 of 172 (65.7%), 104 of 168 (61.9%), 76 of 137 (55.5%), and 87 of 139 (62.6%) in 2000, 2005, 2010, and 2015, respectively.Compared with trials with neutral findings, randomized clinical trials that were positive for the primary end point had lower mean [SD] Pragmatic Explanatory Continuum Index Summary (PRECIS)-2 scores (3.17 [0.70] vs 3.42 [0.66]; P < .001);however, the Cohen d effect size of 0.36 denotes a small difference in the level of pragmatism between trials with positive and neutral findings. 1However, we would caution against the interpretation that the level of pragmatism is the root cause for the neutral results in these trials.Many other factors can play a role in the neutral findings, including the lack of an actual effect, the type of question being addressed, operational considerations, and patient or health system factors, among others.This is analogous to the considerations to trials using surrogate end points (eg, biomarkers), as trials with surrogate markers often yield positive results compared with trials that are focused on clinical end points. 2Pragmatic trials complement explanatory trials, as the intent is different, and we need to be comfortable that not all interventions work as hypothesized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.085 | 0.078 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".